(1) Field of the Invention
The present invention relates to a multimedia retrieval system, and more particularly to a method for updating multimedia feature information such as weights and reliability adaptively to reflect changes of multimedia retrieval environment and a data structure therefor.
(2) Description of the Related Art
Conventional content-based multimedia search technologies use various features such as color histogram and local color for image retrieval. However, the main features for distinguishing images differ from each other. So, various retrieval technologies using a specific weighted feature in each image are recently developed.
Some retrieval technologies provide user interface with dialog boxes such that users define weights of specific image features by themselves through the dialog boxes.
In these technologies, however, it is not easy for general users to set proper multimedia feature weights for image retrieval.
To complement the deficiency of the above retrieval technology, another technique called relevance feedback is used to determine weights of the image features.
In this method, a user browses images first, sorts the browsed images with respect to similarity and non-similarity to a target image, and then feeds back the sort results to the retrieval system. Responsive to the user feed back, the retrieval system automatically sets and adjusts the weights of the image features.
This method has an advantage that the retrieval system uses user feed backs and automatically set the weights of the image features.
In spite of this advantage, in this method the previously learned weight conditions may not contribute to continuous image retrieval as much as it were if the previously used method of similarity measure is changed.
If the method of similarity measure is changed, a new method of similarity measure may use different weight conditions in correlative or absolute ways, and so the learned weights information can be useless in worst case.
Accordingly, if the method of similarity measure is changed, the previous weight conditions should be updated so as to be adaptive to the changed environment.
While, if the weight conditions are learned in long stable environment without change, the stable weight conditions must not be easily changed by the new feedback information such that the retrieval performance is not degraded. And the weight conditions must be adaptive to the changed environment to provide a right retrieval result.
However, these requirements are not met in the retrieval technologies using the dialog box in which the user directly define the feature weight conditions or the relevance feedback in which the retrieval system automatically define the feature weight on the basis of the feedback information from the user.